基于共识的智能体大语言模型框架用于协调关税表编码分类
英文摘要
Researchers propose a novel framework that employs multiple large language model agents working collaboratively to classify Harmonized Tariff Schedule (HTS) codes. The agents engage in a consensus mechanism to improve accuracy over single-model approaches, directly addressing a critical bottleneck in international trade where misclassification leads to shipment delays, fines, and compliance failures. By leveraging agentic collaboration rather than isolated model outputs, the system aims to produce more reliable, standardized code assignments. This work highlights the gap in current manual and rule-based methods and positions LLM-driven consensus as a viable automation strategy for customs operations. The framework is expected to increase efficiency, reduce errors, and streamline regulatory compliance in global supply chains.
中文摘要
研究人员提出一种新的框架，利用多个大语言模型智能体协同工作，对协调关税表（HTS）编码进行分类。各智能体通过共识机制提高准确性，针对国际贸易中的一个关键瓶颈——错误分类会导致货物延误、罚款和合规失败。该系统依靠智能体协作而非单一模型输出，旨在产生更可靠、标准化的编码分配。该研究指出了当前人工和基于规则方法的不足，并将基于大语言模型的共识方法确立为海关作业的可行自动化策略。该框架有望提高效率、减少差错，并简化全球供应链中的法规遵从。
关键要点
Proposes a consensus-based agentic LLM framework for automated HTS code classification.
提出一种基于共识的智能体大语言模型框架，用于自动分类HTS编码。
Multiple LLM agents collaborate and reach consensus to boost classification accuracy and reliability.
多个大语言模型智能体协作并达成共识，以提高分类的准确性和可靠性。
Targets a real-world bottleneck: inaccurate HTS coding causes trade delays, fines, and compliance violations.
针对现实瓶颈：不准确的HTS编码会导致贸易延误、罚款和合规违规。
Positions agentic consensus as a superior alternative to manual, rule-based, or single-model classification systems.
将基于智能体的共识方法定位为优于人工、基于规则或单模型分类系统的替代方案。